Forecasting stock prices using a hierarchical Bayesian approach

Article Abstract:

The Ohlson model is evaluated using quarterly data from stocks in the Dow Jones Index. The estimation of the unknown coefficients in the time series regression model for each company by pooling information across firms is done using a hierarchical Bayesian approach. And the estimation and prediction is done through the usage of the Markov chain Monte Carlo (MCMC) method. The empirical results support the fact that hierarchical Bayesian approach is adequate for making future predictions.

Forecasts, trends, outlooks, Labor Distribution by Employer, Stocks, Forecasts and trends, Statistics, Bayesian statistical decision theory, Bayesian analysis, Market trend/market analysis, Stock prices, Dow Jones World Stock Index (Index)

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A Bayesian threshold nonlinearity test for financial time series

Article Abstract:

A paper on the usage of the reversible-jump Markov chain Monte Carlo methods to calculate the posterior probabilities of two competitive models, the GARCH model and the threshold GARCH models is presented. Posterior evidence favoring the threshold GARCH model indicates threshold nonlinearity or volatile asymmetry.

Methods, Econometric models

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Modelling a traffic network with missing data

Article Abstract:

Research is presented concerning the adaptation of a traffic forecasting model developed by M.E. Whitlock and C.M. Queen in 1998. The use of Markov chain Monte Carol techniques to rectify errors in the original model is discussed.

Telephone and telegraph apparatus, Models, Network management systems, Traffic estimation, Traffic flow

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